Growing concerns regarding domestic security have created a critical need to positively identify individuals as legitimate holders of credit cards, driver's licenses, passports and other forms of identification. The ideal identification process is reliable, fast, and relatively inexpensive. It should be based on modern, high-speed, electronic devices that can be networked to enable fast and effective sharing of information. It should also be compact, portable, and robust for convenient use in a variety of environments, including airport security stations, customs and border crossings, police vehicles, point of sale applications, credit card and ATM applications, home and office electronic transactions, and entrance control sites to secure buildings.
A well established method for identification or authentication is to compare biometric characteristics of an individual with a previously obtained authentic biometric of the individual. Possible biometric characteristics include ear shape and structure, facial characteristics, facial or hand thermograms, iris and retina structure, handwriting, and friction ridge patterns of skin such as fingerprints, palm prints, foot prints, and toe prints. A particularly useful biometric system uses fingerprints for individual authentication and identification. (Maltoni, Maio, Jain, and Prabhakar, “Handbook of Fingerprint Recognition”, Springer, 2003, chapter 1, and David R. Ashbaugh, “Quantitative-Qualitative Friction Ridge Analysis”, CRC Press, 1999).
Fingerprints have traditionally been collected by rolling an inked finger on a white paper card. Since this traditional process clearly fails to meet the criteria listed above, numerous attempts have been made to develop an electronically imaged fingerprint method to address new security demands. These modern methods typically use, as a key component, a solid-state device such as a capacitive or optical sensor to capture the fingerprint image in a digital format. By using a solid-state imager as part of a fingerprint identification apparatus, a fingerprint can be collected conveniently and rapidly during a security check, for example, and subsequently correlated, in near real-time, to previously trained digital fingerprints in an electronic database. The database can reside on a computer at the security check point, on a secure but portable or removable storage device, on a remotely networked server, or as a biometric key embedded into a smartcard, passport, license, birth certificate, or other form of identification.
The topological features of a typical finger comprise a pattern of ridges separated by valleys, and a series of pores located along the ridges. The ridges are typically 100 to 300 μm wide and can extend in a number of different swirl-like patterns for several mm to one or more cm. The ridges are separated by valleys with a typical ridge-valley period of approximately 250-500 μm. Pores, roughly circular in cross section, range in diameter from about 40 μm to 200 μm, and are aligned along the ridges. The patterns of both ridges/valleys and pores are believed to be unique to each fingerprint. No currently available commercial fingerprint acquisition technique is able to resolve pores and ridge deviation details to a degree necessary to use this vastly larger amount of information as a biometric key. Accordingly, present-day automatic fingerprint identification procedures use only portions of ridge and valley patterns, called minutiae, such as ridge ending-points, deltoids, bifurcations, crossover points, and islands, which are found in almost every fingerprint (Maltoni, Maio, Jain, and Prabhakar, “Handbook of Fingerprint Recognition”, Springer, 2003, chapter 3). Extraction and comparison of minutiae is the basis of most current automatic fingerprint analysis systems.
There are several important limitations with minutiae-based methods of automatic fingerprint analysis. In order to collect enough minutiae for reliable analysis a relatively large area, at least 0.50×0.50 inches, good quality, fingerprint, or latent image of a fingerprint must be available. Large prints are often collected by rolling an inked finger on a white card, and subsequently scanning the inked image into an electronic database. This manual procedure is an awkward and time consuming process that requires the assistance of a trained technician. Automated methods for collecting large fingerprints usually require mechanically complicated and expensive acquisition devices. Large area fingerprints suffer from distortions produced by elastic deformations of the skin so that the geometrical arrangements between minutiae points vary from image to image of the same finger, sometimes significantly. In addition, forensic applications can involve small, poor quality, latent prints that contain relatively few resolved minutiae so that reliable analysis based on a limited number of minutiae points is quite difficult.
Minutiae comparison ignores a significant amount of structural information that may be used to enhance fingerprint analysis. Since the typical fingerprint contains between 7 to 10 times as many pores as minutiae, techniques that include both pores and minutiae should greatly improve matching compared to techniques that use only minutiae. This highly detailed information is referred to in the industry as “level three detail,” and is the basis of most forensic level analysis of latent images left at a crime scene, where the latent does not contain enough minutiae to make an accurate identification. Stosz and Alyea (J. D. Stosz, L. A. Alyea, “Automated system for fingerprint authentication using pores and ridge structures”, Proc. SPIE, vol 2277, 210-223, 1994) have confirmed this expectation by showing that the use of pores combined with minutiae improves the accuracy of fingerprint matching and allows successful analysis of relatively small prints. Their image sensor used a common prism-based configuration, a high-resolution Charge Coupled Device (CCD) video camera, and a macro lens to provide the resolution needed to image pores. After acquisition, the gray-scale images are converted to a binary format and then processed further to produce a skeleton image from which minutiae and pores are identified. Fingerprints are compared by independent correlations between pores and minutiae extracted from the various images.